code: def test_predict_correct_output_dtype():
      X, y = make_classification(chunks=100)
      X_ddf = dd.from_dask_array(X)
  
      base = LinearRegression(n_jobs=1)
      base.fit(X, y)
  
      wrap = ParallelPostFit(base)
  
      base_output = base.predict(X_ddf.compute())
      wrap_output = wrap.predict(X_ddf)
  
      assert wrap_output.dtype == base_output.dtype
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier
  from sklearn.naive_bayes import CategoricalNB
  from sklearn.preprocessing import OneHotEncoder
  from dask_ml.datasets import make_classification
  from dask_ml.utils import assert_eq_ar, assert_estimator_equal
  from dask_ml.wrappers import ParallelPostFit
label_desc: Use image node_id 7 for calling a global function with example usage: test_predict_correct_output_dtype() without return types